RAG Systems & Knowledge Agents

Enterprise RAG (Retrieval-Augmented Generation) pipelines that let AI agents securely retrieve, reason over, and act on your internal knowledge — documents, databases, APIs — in real time.

What Is a RAG System?

RAG (Retrieval-Augmented Generation) is a technique that gives AI models access to your private knowledge base — not just their training data. When a user asks a question, the system retrieves the most relevant documents from your vector database, then generates a grounded, accurate answer.

The result: AI that actually knows your business. Your products, your processes, your contracts, your 10 years of internal research — all queryable in seconds.

Bytolix builds enterprise RAG systems using LlamaIndex, Pinecone, Azure AI Search, and pgvector — with hybrid search, reranking, and citation tracing built in.

What We Build Into Every RAG System

  • Hybrid search — semantic + keyword search for maximum recall
  • Reranking — cross-encoder reranking for precision at the top
  • Citation tracing — every answer links back to the source document
  • Access control — role-based retrieval so agents only see what users should
  • Evaluation harness — RAGAS scoring to measure faithfulness and relevance

RAG Deployments in Production

Enterprise Knowledge Base

10 years of internal research made instantly queryable. What took 3 days now takes 3 minutes. Full audit trail for compliance. Deployed for AlphaLogic.

Contract Intelligence

Legal agents that ingest contracts, chunk by clause, embed with context, and answer questions like "what are our termination clauses with Vendor X?" in seconds.

Product Support Agent

RAG-powered support agent trained on product documentation, past tickets, and runbooks. Resolves 70% of tickets without human escalation.

Let Your Data Work For You

Your enterprise knowledge is your most valuable asset. A RAG system makes it instantly accessible to AI agents and your entire team. Let's scope yours.

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